Search Results - "data science"

EiA AgWise: A data science solution for site-specific agronomic recommendations by Chernet, M., Pypers, P., Tilaye, A., Tibebe, D., Bendito, E., Mkuhlani, S., Srivastava, A., Ghosh, P., Chimonyo, V., Gonzalez, A., Leroux, L., Llanos, L., Mugo, J., Sila, A., Perez, C., Persson, K., Moreno, P., Ramirez, J., Ghosh, A., Silva, J., Hijmans, R., Urfels, Anton, Kheir, A., Abera, W., Barasa, S., Mudereri, B., Bonilla, C., Mesfin, T., Agudelo, D., Patil, M., Devare, Medha

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Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions by Harris, E., Barthel, M., Leitner, Sonja, Ouma, T., Agredazywczuk, P., Otinga, A., Njoroge, R., Oduor, Collins, Oluoch, K. C.

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A Decade of Digital Innovations and Agri-Startups by Rupavatharam, Srikanth

Subjects: “…data science…”
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Harnessing data science to improve integrated management of invasive pest species across Africa: an application to Fall armyworm (Spodoptera frugiperda) (J.E.... by Guimapi, R.A., Niassy, S., Mudereri, B.T., Abdel-Rahman, E.M., Tepa-Yotto, G., Subramanian, S., Mohamed, S.A., Thunes, K.H., Kimathi, E.K., Agboka, K., Tamò, Manuele, Rwaburindi, J.C., Hadi, B., Elkahky, M., Saethre, M.G., Belayneh, Y.T., Ekesi, S., Kelemu, S., Tonnang, Henri E.Z.

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Mega program on Digital and Data Innovation - ICT4Ag 2024 by Rupavatharam, Srikanth

Subjects: “…data science…”
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Puesta en valor de la información de herbarios de INTA by Morales, Matias, Borrelli, Laura Beatriz, Arroyo, Daniel Nicolas, Noguera, Justo Maximiliano, Digilio, Ariana, Lagorio, Paula, López, Alicia, Nuñez, Francisco, Tomanek, Emmanuel, Godoy, Ines, Zamuz, Jose Antonio

Subjects: “…Data Science…”
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Female Drone Pilots: moving from an exception to the rule in Tanzania by Funja, Rose

“…A young African woman thriving and growing her business in the male dominated field of drone piloting and data science. Rose Funja is also giving back by working to promote young womens' participation in STEM.…”
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Young scientists benefit from cross-CGIAR expertize in digital innovation for agriculture by CGIAR

“…The online course is targeted at young scientists and researchers pursuing their bachelor’s degree in either agriculture, environmental sciences or data science.…”
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Editorial: Genome wide association studies and genomic selection for crop improvement in the era of Big Data by Bentley, Alison R., Chen, Charles, D’Agostino, Nunzio

“…More and more studies are taking advantage of these scientific advances and of data science techniques to uncover the genome-to-phenome relationship and unlock the breeding potential of plant genetic resources. …”
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Smallholder farmers, food security, and livelihoods: Exploring trade offs and synergies using farm household characterization data by Wijk, Mark T. van, Hammond, James, Frelat, Romain, Fraval, Simon, Teufel, Nils, Tavenner, Katie, Henderson, Benjamin B.

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Unpacking the ‘black box’ of public expenditure data in Africa: Quantification of agricultural spending using Mozambique’s budget reports by Mogues, Tewodaj, Caceres, Leonardo

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Relevant loci for milk production in dairy cattle, obtained by machine learning algorithms by Raschia, Maria Agustina, Ríos, Pablo J., Maizon, Daniel Omar, Demitrio, Daniel Arturo, Poli, Mario Andres

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Diploma Course on Digital Agriculture in Guatemala by Jiménez, Daniel, Martínez, Jesús David, Muñoz, Armando, García, Ramiro, Borrayo, Andrea, Ramírez Villegas, Julián Armando, Sandoval, Victor

“…The 90-hour diploma course is organized in to five modules (i) Human-centered design and digital inclusion, (ii) Data collection, data management, and data integration, (iii) Data science, (iv) Crop modeling, and (v) scaling digital innovations. …”
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Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning by Mcbride, Linden, Barrett, Christopher B., Browne, Christopher, Hu, Leiqiu, Liu, Yanyan, Matteson, David S., Sun, Ying, Wen, Jiaming

“…Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. …”
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Escaping poverty traps and unlocking prosperity in the face of climate risk: Lessons from Index-Based Livestock Insurance by Jensen, Nathaniel D., Fava, Francesco P., Mude, Andrew G., Barrett, Christopher B., Wandera-Gache, Brenda, Vrieling, Anton, Taye, Masresha, Takahashi, Kazushi, Lung, Felix, Ikegami, Munenobu, Ericksen, Polly J., Chelanga, Philemon, Chantarat, Sommarat, Carter, Michael, Bashir, Hassan, Banerjee, Rupsha R.

“…General insights are provided on i) the economics of poverty, risk management, and drylands development; ii) the evolving use of modern remote sensing and data science tools in development; iii) the science of scaling; and iv) the value and challenges of integrating research with operational implementation to tackle development and humanitarian challenges in some of the world's poorest regions.…”
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Sharing research data in a connected world for connected decisions: Lessons learned from the CGIAR Research Program on Water, Land and Ecosystems (WLE) by CGIAR Research Program on Water, Land and Ecosystems

“…Trends in data science demonstrate the utility of big, open access data in addressing global challenges such as land degradation, food and nutrition security, biodiversity loss and climate change. …”
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Enviromics: bridging different sources of data, building one framework by Costa-Neto, Germano, Fritsche-Neto, Roberto

“…Enviromics is the field of applied data science that integrates databases of environmental factors into biostatistics and quantitative genetics. …”
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Using explainable machine learning techniques to unpack farm-level management x climate interactions by Ramírez Villegas, Julián Armando, Jaimes, Diana, Gonzalez Rodriguez, Carlos Eduardo, Llanos, Lizeth, Jimenez, Daniel, Gardeazabal, Andrea, Estrada, Oscar, Nuñez, Daniel

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Governing agricultural data: Challenges and recommendations by Devare, Medha, Arnaud, Elizabeth, Antezana, Erick, King, Brian

“…The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. …”
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Predicting climate smart agriculture (CSA) practices using machine learning: A prime exploratory survey by Noma, Freddy, Babu, Suresh Chandra

“…And proposing a theoretical approach that grounds the selection of data (i.e. input and response variables) with well stablished theories on adaptation decision making process; with the aim of demonstrating ways of improving data science and ML publication quality in the field of agricultural economics. …”
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